{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1> Analyzing convention speeches with Google Language API </h1>\n",
    "\n",
    "This notebook accompanies my Medium article: <a href=\"https://medium.com/@lakshmanok/is-this-presidential-election-more-negative-than-years-past-yes-ca254e35eb9#.krrlhkryr\"> Is this presidential election more negative then years past? Yes. </a>\n",
    "\n",
    "<h2> API Key </h2>\n",
    "To repeat my analysis, you need a Google Cloud Platform account (use the free trial). Then, visit <a href=\"http://code.google.com/apis/console\">API console</a>, choose \"Credentials\" on the left-hand menu.  Choose \"API Key\" and generate a server key for your application. You should probably restrict it by IP address to prevent abuse, but for now, just  leave that field blank and delete the API key after trying out this demo.\n",
    "\n",
    "Copy-paste your API Key here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "APIKEY=\"AIzaSyBNa0Hw5_SZpmQP2-iXgUfchVHa4Ot956M\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Note: Make sure you got an API Key and pasted it above. Mine won't work for you </b>\n",
    "\n",
    "From the same API console, choose \"Dashboard\" on the left-hand menu and \"Enable API\".\n",
    "\n",
    "Finally, because we are calling the APIs from Python (clients in many other languages are available), let's install the Python package (it's not installed by default on Datalab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already up-to-date: google-api-python-client in /usr/local/lib/python2.7/dist-packages\n",
      "Requirement already up-to-date: six<2,>=1.6.1 in /usr/local/lib/python2.7/dist-packages (from google-api-python-client)\n",
      "Requirement already up-to-date: uritemplate<1,>=0.6 in /usr/local/lib/python2.7/dist-packages (from google-api-python-client)\n",
      "Requirement already up-to-date: httplib2<1,>=0.8 in /usr/local/lib/python2.7/dist-packages (from google-api-python-client)\n",
      "Collecting oauth2client (from google-api-python-client)\n",
      "  Downloading oauth2client-3.0.0.tar.gz (77kB)\n",
      "\u001b[K    100% |################################| 81kB 660kB/s \n",
      "\u001b[?25hRequirement already up-to-date: simplejson>=2.5.0 in /usr/local/lib/python2.7/dist-packages (from uritemplate<1,>=0.6->google-api-python-client)\n",
      "Requirement already up-to-date: pyasn1>=0.1.7 in /usr/local/lib/python2.7/dist-packages (from oauth2client->google-api-python-client)\n",
      "Requirement already up-to-date: pyasn1-modules>=0.0.5 in /usr/local/lib/python2.7/dist-packages (from oauth2client->google-api-python-client)\n",
      "Requirement already up-to-date: rsa>=3.1.4 in /usr/local/lib/python2.7/dist-packages (from oauth2client->google-api-python-client)\n",
      "Building wheels for collected packages: oauth2client\n",
      "  Running setup.py bdist_wheel for oauth2client ... \u001b[?25l-\b \bdone\n",
      "\u001b[?25h  Stored in directory: /root/.cache/pip/wheels/b3/44/e9/e56c5a2ca8869305f38254df012fb16b2807eb9d5d55291e8b\n",
      "Successfully built oauth2client\n",
      "Installing collected packages: oauth2client\n",
      "  Found existing installation: oauth2client 2.2.0\n",
      "    Uninstalling oauth2client-2.2.0:\n",
      "      Successfully uninstalled oauth2client-2.2.0\n",
      "Successfully installed oauth2client-3.0.0\n",
      "\u001b[33mYou are using pip version 8.1.1, however version 8.1.2 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "# Copyright 2016 Google Inc.\n",
    "# Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at\n",
    "# http://www.apache.org/licenses/LICENSE-2.0\n",
    "# Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.\n",
    "!pip install --upgrade google-api-python-client"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2> Sentiment analysis with Language API </h2>\n",
    "\n",
    "Let's evaluate the sentiment of nomination acceptance speeches for the last 5 election cycles. The text of the speeches will be downloaded from U. California, Santa Barbara."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "POLARITY=0.2 MAGNITUDE=37.4 SPEAKER=Hillary Clinton\n",
      "POLARITY=0 MAGNITUDE=33.5 SPEAKER=Donald Trump\n",
      "POLARITY=0.3 MAGNITUDE=40.7 SPEAKER=Barack Obama\n",
      "POLARITY=0.4 MAGNITUDE=31.7 SPEAKER=Mitt Romney\n",
      "POLARITY=0.1 MAGNITUDE=28.1 SPEAKER=Barack Obama\n",
      "POLARITY=0.3 MAGNITUDE=39.7 SPEAKER=John McCain\n",
      "POLARITY=0.4 MAGNITUDE=39.8 SPEAKER=John Kerry\n",
      "POLARITY=0.3 MAGNITUDE=37.8 SPEAKER=George W Bush\n",
      "POLARITY=0.4 MAGNITUDE=33.2 SPEAKER=Al Gore\n",
      "POLARITY=0.4 MAGNITUDE=40.7 SPEAKER=George W Bush\n"
     ]
    }
   ],
   "source": [
    "from googleapiclient.discovery import build\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import urllib2\n",
    "\n",
    "lservice = build('language', 'v1beta1', developerKey=APIKEY)\n",
    "speeches = [\n",
    "  ['Hillary Clinton', 'D', 2016, 'http://www.presidency.ucsb.edu/ws/index.php?pid=118051'],\n",
    "  ['Donald Trump', 'R', 2016, 'http://www.presidency.ucsb.edu/ws/index.php?pid=117935'],\n",
    "  ['Barack Obama', 'D', 2012, 'http://www.presidency.ucsb.edu/ws/index.php?pid=101968'],\n",
    "  ['Mitt Romney', 'R', 2012, 'http://www.presidency.ucsb.edu/ws/index.php?pid=101966'],\n",
    "  ['Barack Obama', 'D', 2008, 'http://www.presidency.ucsb.edu/ws/index.php?pid=78284'],\n",
    "  ['John McCain', 'R', 2008, 'http://www.presidency.ucsb.edu/ws/index.php?pid=78576'],\n",
    "  ['John Kerry', 'D', 2004, 'http://www.presidency.ucsb.edu/ws/index.php?pid=25971'],\n",
    "  ['George W Bush', 'R', 2004, 'http://www.presidency.ucsb.edu/ws/index.php?pid=72727'],\n",
    "  ['Al Gore', 'D', 2000, 'http://www.presidency.ucsb.edu/ws/index.php?pid=25963'],\n",
    "  ['George W Bush', 'R', 2000, 'http://www.presidency.ucsb.edu/ws/index.php?pid=25954']\n",
    "]\n",
    "\n",
    "sentiment = []\n",
    "for (speaker, party, year, url) in speeches:\n",
    "  text_of_speech = urllib2.urlopen(url).read()\n",
    "  response = lservice.documents().analyzeSentiment(\n",
    "    body={\n",
    "      'document': {\n",
    "         'type': 'HTML',\n",
    "         'content': unicode(text_of_speech, errors='ignore')\n",
    "      }\n",
    "    }).execute()\n",
    "  polarity = response['documentSentiment']['polarity']\n",
    "  magnitude = response['documentSentiment']['magnitude']\n",
    "  print('POLARITY=%s MAGNITUDE=%s SPEAKER=%s' % (polarity, magnitude, speaker))\n",
    "  sentiment.extend([speaker, party, year, float(polarity), float(magnitude)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           speaker party  year  polarity  magnitude\n",
      "0  Hillary Clinton     D  2016       0.2       37.4\n",
      "1     Donald Trump     R  2016       0.0       33.5\n",
      "2     Barack Obama     D  2012       0.3       40.7\n",
      "3      Mitt Romney     R  2012       0.4       31.7\n",
      "4     Barack Obama     D  2008       0.1       28.1\n",
      "5      John McCain     R  2008       0.3       39.7\n",
      "6       John Kerry     D  2004       0.4       39.8\n",
      "7    George W Bush     R  2004       0.3       37.8\n",
      "8          Al Gore     D  2000       0.4       33.2\n",
      "9    George W Bush     R  2000       0.4       40.7\n"
     ]
    }
   ],
   "source": [
    "df = pd.DataFrame(data=np.array(sentiment).reshape(10,5),\n",
    "                  columns=['speaker', 'party', 'year', 'polarity', 'magnitude'])\n",
    "for col in ['year', 'polarity', 'magnitude']:\n",
    "  df[col] = pd.to_numeric(df[col])\n",
    "print df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa538eaefd0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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5my1qtVqNlJQUJCUlQZIkxMfHIyAgABkZGVCpVEhISMCuXbuQnZ0NJycnODs7G8rY1LpE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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa538f71990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = df.sort_values('year')\n",
    "df.plot(x='speaker',  y='polarity', kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa53833fc50>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GC7ht27bYvXt3udcff/xxbNq0yRyZiIiIrB5nwiIiIhKABUxERCQAC5iIiEgAFjAREZEA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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa538f71c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(x='speaker',  y='magnitude', kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year         2008.00\n",
       "polarity        0.28\n",
       "magnitude      35.84\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['party'] == 'D'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year         2008.00\n",
       "polarity        0.28\n",
       "magnitude      36.68\n",
       "dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['party'] == 'R'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
